Go back to the Preprocessing page. This link might be useful to keep track of the files created during the preprocessing.

Let us set some global options for all code chunks in this document.

knitr::opts_chunk$set(
  message = FALSE,    # Disable messages printed by R code chunks
  warning = FALSE,    # Disable warnings printed by R code chunks
  echo = TRUE,        # Show R code within code chunks in output
  include = TRUE,     # Include both R code and its results in output
  eval = TRUE,       # Evaluate R code chunks
  cache = FALSE,       # Enable caching of R code chunks for faster rendering
  fig.align = "center",
  out.width = "70%",
  fig.dim = c(8,8),
  retina = 2,
  error = TRUE,
  collapse = FALSE
)
rm(list = ls())
set.seed(1982)

1 Import libraries

# Install R-INLA package
# install.packages("INLA",repos = c(getOption("repos"),INLA ="https://inla.r-inla-download.org/R/testing"), dep = TRUE)
# Update R-INLA package
# inla.upgrade(testing = TRUE)
# Install inlabru package
# remotes::install_github("inlabru-org/inlabru", ref = "devel")
# Install rSPDE package
# remotes::install_github("davidbolin/rspde", ref = "devel")
# Install MetricGraph package
# remotes::install_github("davidbolin/metricgraph", ref = "devel")

library(INLA)
library(inlabru)
library(rSPDE)
library(MetricGraph)

library(plotly)
library(dplyr)
library(tidyr)
library(sf)
library(mapview)
library(listviewer)
library(jsonlite)
library(ggplot2)

library(here) # here() starts from the home directory
library(rmarkdown)
library(grateful) # Cite all loaded packages

rm(list = ls()) # Clear the workspace
set.seed(1982) # Set seed for reproducibility

2 Load the data

# Load the data
load(here("data_files/day7142128hour13noconsecutivezeroes.RData")) # just to get the crs

# Load the network from tomtom
load(here("data_files/tomtom.RData"))

2.1 Explore the data

tomtom |> head(5) |> paged_table()
tomtom |> dim()
## [1] 67199    39
ggplot(data = tomtom) +
  geom_sf(aes(color = SpeedLimit)) +
  scale_color_viridis_c(option = "D") +
  ggtitle("Street network") + 
  theme_minimal() + 
  theme(text = element_text(family = "Palatino"))

3 Filter and format the street network data

# Build polygon to cut the network and the data
polygon <- st_multipoint(c(st_point(c(-122.53000, 37.69702)),
                          st_point(c(-122.37000, 37.69702)),
                          st_point(c(-122.37000, 37.82600)),
                          st_point(c(-122.53000, 37.82600)))) %>%
  st_cast("POLYGON") %>%
  st_sfc(crs = st_crs(df)) # df dataset needs to be loaded to get the crs

# Filter and prepare the network data
from.tomtom <- tomtom %>%
  dplyr::select(-Id, -Segment.Id, -NewSegId, -timeSet, -dateRange, -standardDeviationSpeed, -travelTimeStandardDeviation) %>% # Remove unnecessary columns
  filter(FRC != "7") %>% # Remove tomtom class 5, 6 and 7 
  mutate(value = SpeedLimit, road_type = paste("class_", FRC, sep = ""), aux = paste("class_", FRC, sep = "")) %>% # Create road_type and aux variables
  pivot_wider(names_from = aux, values_from = value, values_fill = list(value = 0)) %>% # Use aux and value to create one-hot encoding for road_type
  mutate(upto1 = class_0 + class_1) %>% # Create upto1 variable
  mutate(upto3 = upto1 + class_3) %>% # Create upto3 variable
  mutate(upto4 = upto3 + class_4) %>% # Create upto4 variable
  mutate(upto5 = upto4 + class_5) %>% # Create upto5 variable
  mutate(upto6 = upto5 + class_6) %>% # Create upto6 variable
  mutate(Length  = Length/1000) %>%  # Transform Length from meters to kilometers
  mutate(density = sampleSize/Length) %>% # Create density variable (per day)
  mutate(density_per_hour = density/24) %>% # Create density_per_hour variable
  st_transform(crs = st_crs(df)) %>% # Transform to the same crs as the data
  st_filter(x = ., y = polygon, .predicate = st_within) # Filter by the polygon

road_types <- paste0("FRC", paste(sort(unique(from.tomtom$FRC)), collapse = ""))

# Get the weights and edges
weights <- from.tomtom %>% st_drop_geometry()
edges <- from.tomtom$geometry

3.1 Explore the data

Data frame from.tomtom is the last version of the data before building the graph. Below we explore its structure and show how it looks like.

from.tomtom |> head(5) |> paged_table()
from.tomtom |> dim()
## [1] 44319    46
ggplot(data = from.tomtom) +
  geom_sf(aes(color = SpeedLimit)) +
  scale_color_viridis_c(option = "D") +
  ggtitle("Filtered street network") + 
  theme_minimal() + 
  theme(text = element_text(family = "Palatino"))

4 Build and save the graph

# Build the graph
graph <- graph_components$new(edges = edges, which_longlat = "sf", longlat = TRUE, edge_weights = weights)

# Get the largest connected component
sf_graph = graph$get_largest()

# Save the graph (notice that it has no data)
save(sf_graph, file = here("data_files/graph_construction_on_27JUN2024_FRC013456.RData"))

4.1 Explore the data

Below we show how the graph looks like.

sf_graph$plot(vertex_size = 0, 
              edge_width = 1, 
              edge_weight = "SpeedLimit", 
              edge_width_weight = "SpeedLimit", 
              edge_color = "SpeedLimit",
              scale_color_weights = ggplot2::scale_color_viridis_c(option = "D")) +
  ggtitle("Metric graph of the street network") + 
  theme_minimal() + 
  theme(text = element_text(family = "Palatino"))

graph_w <- sf_graph$get_edge_weights()
graph_w |> head(5) |> paged_table()
graph_w |> dim()
## [1] 44284    45

5 References

cite_packages(output = "paragraph", out.dir = ".")

We used R version 4.4.0 (R Core Team 2024) and the following R packages: here v. 1.0.1 (Müller 2020), htmltools v. 0.5.8.1 (Cheng et al. 2024), INLA v. 24.6.27 (Rue, Martino, and Chopin 2009; Lindgren, Rue, and Lindström 2011; Martins et al. 2013; Lindgren and Rue 2015; De Coninck et al. 2016; Rue et al. 2017; Verbosio et al. 2017; Bakka et al. 2018; Kourounis, Fuchs, and Schenk 2018), inlabru v. 2.10.1.9010 (Yuan et al. 2017; Bachl et al. 2019), knitr v. 1.47 (Xie 2014, 2015, 2024), listviewer v. 4.0.0 (de Jong, Gainer, and Russell 2023), mapview v. 2.11.2 (Appelhans et al. 2023), MetricGraph v. 1.3.0.9000 (Bolin, Simas, and Wallin 2023b, 2023a, 2023c, 2024; Bolin et al. 2023), patchwork v. 1.2.0 (Pedersen 2024), plotly v. 4.10.4 (Sievert 2020), rmarkdown v. 2.27 (Xie, Allaire, and Grolemund 2018; Xie, Dervieux, and Riederer 2020; Allaire et al. 2024), rSPDE v. 2.3.3.9000 (Bolin and Kirchner 2020; Bolin and Simas 2023; Bolin, Simas, and Xiong 2023), scales v. 1.3.0 (Wickham, Pedersen, and Seidel 2023), sf v. 1.0.16 (Pebesma 2018; Pebesma and Bivand 2023), tidyverse v. 2.0.0 (Wickham et al. 2019), TSstudio v. 0.1.7 (Krispin 2023), xaringanExtra v. 0.8.0 (Aden-Buie and Warkentin 2024).

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